@article {3410,
	title = {Measuring change at Earth{\textquoteright}s surface: On-demand vertical and three- dimensional topographic differencing implemented in OpenTopography},
	journal = {Geosphere},
	year = {2021},
	month = {May},
	abstract = {Topographic differencing measures landscape change by comparing multitemporal high-resolution topography data sets. Here, we focused on two types of topographic differencing: (1) Vertical differencing is the subtraction of digital elevation models (DEMs) that span an event of interest. (2) Three-dimensional (3-D) differencing measures surface change by registering point clouds with a rigid deformation. We recently released topo-graphic differencing in OpenTopography where users perform on-demand vertical and 3-D differencing via an online interface. OpenTopography is a U.S. National Science Foundation{\textendash}funded facility that provides access to topographic data and processing tools. While topographic differencing has been applied in numerous research studies, the lack of standardization, particularly of 3-D differencing, requires the customization of processing for individ-ual data sets and hinders the community{\textquoteright}s ability to efficiently perform differencing on the growing archive of topography data. Our paper focuses on streamlined techniques with which to efficiently difference data sets with varying spatial resolution and sensor type (i.e., optical vs. light detection and ranging [lidar]) and over variable landscapes. To optimize on-demand differencing, we considered algorithm choice and displacement resolution. The optimal resolution is controlled by point density, landscape characteristics (e.g., leaf-on vs. leaf-off), and data set quality. We provide processing options derived from metadata that allow users to produce optimal high-quality results, while experienced users can fine tune the parameters to suit their needs. We anticipate that the differencing tool will expand access to this state-of-the-art technology, will be a valuable educational tool, and will serve as a template for differencing the growing number of multitemporal topography data sets.},
	issn = {1553-040X},
	doi = {10.1130/GES02259.1},
	url = {https://doi.org/10.1130/GES02259.1},
	author = {Scott, Chelsea and Phan, Minh and Nandigam, Viswanath and Crosby, Christopher and Arrowsmith, J. Ramon}
}
@article {3399,
	title = {Measuring success for a future vision: Defining impact in science gateways/virtual research environments},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {n/a},
	year = {2020},
	abstract = {Summary Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.},
	keywords = {futuristic vision, measuring impact, science gateways, success metrics, virtual research environments},
	doi = {https://doi.org/10.1002/cpe.6099},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6099},
	author = {Calyam, Prasad and Wilkins-Diehr, Nancy and Miller, Mark and Brookes, Emre H. and Arora, Ritu and Chourasia, Amit and Jennewein, Douglas M. and Nandigam, Viswanath and Drew LaMar, M. and Cleveland, Sean B. and Newman, Greg and Wang, Shaowen and Zaslavsky, Ilya and Cianfrocco, Michael A. and Ellett, Kevin and Tarboton, David and Jeffery, Keith G. and Zhao, Zhiming and Gonz{\'a}lez-Aranda, Juan and Perri, Mark J. and Tucker, Greg and Candela, Leonardo and Kiss, Tamas and Gesing, Sandra}
}
@article {3329,
	title = {Measuring Success: How Science Gateways Define Impact},
	journal = {Proceedings of Gateways 2019},
	year = {2019},
	month = {Sep},
	url = {http://par.nsf.gov/biblio/10116256},
	author = {Wilkins-Diehr, Nancy and Miller, Mark and Brookes, Emre H and Arora, Ritu and Chourasia, Amit and Calyam, Prasad and Jennewein, Douglas M and Nandigam, Viswanath and LaMar, M Drew and Cleveland, Sean B and et al.}
}
